on May 2, 2011
This book is unique in the extensive market of books on analysis of survey data. Most data collected on finite populations are selected with unequal probabilities of selection, strata and clusters, but most regression textbooks assume a simple random sample. This is the first full-length textbook that deals with subclass analyses, categorical data analysis, and various generalized linear models (from linear regression through hierarchical models) and complex survey designs at a statistical level accessible to most graduate students or data analysts. Weight creation and multiple imputation are also covered.
Readers will not be scared off by 'too many' formulas in this book. Although formulas are used throughout the book, there is not a great deal of detailed statistical theory presented; additionally, the 'Theory Boxes' provide enough information for more statistically inclined readers to know where to turn for more information. The book is best used for a more advanced statistical models class, after students have taken their basic regression/correlation class (and possibly after a categorical data class). The homework assignments at the end of each chapter are a useful addition to the text, with the required data sets available on the book's website. I find that the homework assignments are best supplemented with additional examples with other data sets, especially for classes taught repeatedly, but they are a great starting place.
I strongly recommend this book to anyone wanting a 'how to' book for conducting and interpreting analyses on complex survey data, supplemented with extensive documentation on model fitting and diagnostics under a complex survey design (where they are available). It is immediately useful, with Stata code for all of the analyses provided in the text and SAS, Stata, MPlus, R, SUDAAN, WesVar, and SPSS code on the website.
on December 25, 2010
Applied Survey Data Analysis (ASDA) is a crystal-clear survey of modern techniques for analyzing complex survey data. Note the word "analyzing". This is not a text on sampling methods per se. Rather, it is a guide to using existing data sets that result from a complex survey design that employs weighting, clustering, and stratification. The authors demonstrate how a correct analysis should be undertaken. In doing so, they review descriptive statistics, categorical methods, regression analysis (linear and logistic), survival analysis, and multiple imputation. Most examples use Stata, but some are in SAS.
The level of mathematical sophistication is not high, although "theory boxes" are interspersed to add additional detail. Anyone who is challenged by the mathematical level of this book probably should not be working with survey data in the first place.
In sum, this is an important -- and very well written -- contribution to the literature on survey data analysis.
on August 23, 2011
I bought this book in preparation for analyzing NHANES data for the first time.
To give an idea of my background: My formal biostatistics training is quite limited to what I got through experience on different research projects and through medical school. I use Stata 12 in a very basic way, and this was the first project using a large dataset that I did my own analysis for from start to finish. I've worked with weighted survey data before, but another statistician was doing all the actual coding.
I thought this book was pretty balanced between theory and practical issues. A lot of the theory was over my head, but certain parts were extremely illuminating and useful to read through. They do specify at least two semester of graduate level statistics or something like that as a prerequisite, but obviously I don't have that and I still found the book useful. The real value of the book lies in its numerous and detailed examples. The authors actually use NHANES data (and other national datasets) to work through their examples. They walk you through many different types of analyses, include multiple linear regression, multiple logistic regression, etc. Many of these examples are very detailed, and they build the whole model step-by-step and explain the rationale behind each step and decision. The book is extremely well organized, so that by flipping through the table of contents you can immediately find the relevant section for what you want to do with the data, read through the example, and apply the Stata code directly to your own analysis.
NHANES does have tutorials about how to work through their data; although I also found those to be useful and essential, I think that this book is superior because it does give more background about why you need to run certain types of analyses and tests rather than others.
I can't believe my university library didn't have this book--I think it's a totally worthwhile purchase for anyone preparing to work with large national datasets.
on November 29, 2015
This is my "go to" book for survey data analysis. I used it to learn to analyze complex surveys (BRFSS, MCBS, NHANES, HRS), got my lab to buy it and the data analyst also found it quite useful. A good mixture of theory and practical application which I used for both SAS and STATA. I still refer back to it if I have a practical question or if I need to explain the "why" of something related to complex survey analysis to someone else. I have had interactions with one of the authors who was very helpful.
Cannot really comment on their R code since I have not used it for that. (I do think conceptually it is much better than Lumley's book on complex survey analysis in R.)